
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase_    :			Dict    =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")

@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    Optional[str]          =			field(
			    default="""cifar10"""  ,							metadata={"""help""": """Name of a dataset from the datasets package"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """The column name of the images in the files."""}	)
			__UpperCamelCase   :    Optional[str]          =			field(default=_A  ,							metadata={"""help""": """A folder containing the training data."""}	)
			__UpperCamelCase   :    Optional[str]          =			field(default=_A  ,							metadata={"""help""": """A folder containing the validation data."""}	)
			__UpperCamelCase   :    Optional[float]          =			field(
			    default=0.15  ,							metadata={"""help""": """Percent to split off of train for validation."""}	)
			__UpperCamelCase   :    Optional[int]          =			field(
			    default=_A  ,							metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }  ,							)
			__UpperCamelCase   :    Optional[int]          =			field(
			    default=_A  ,							metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }  ,							)

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				List[str]						=    {}
							if self.train_dir is not None:
											UpperCamelCase_:				str						=    self.train_dir
							if self.validation_dir is not None:
											UpperCamelCase_:				Dict						=    self.validation_dir
							UpperCamelCase_:				str						=    data_files if data_files else None




@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    str          =			field(
			    default=_A  ,							metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
			        )
			    }  ,							)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    }  ,							)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}	)
			__UpperCamelCase   :    str          =			field(
			    default="""main"""  ,							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,							)
			__UpperCamelCase   :    str          =			field(default=_A  ,							metadata={"""help""": """Name or path of preprocessor config."""}	)
			__UpperCamelCase   :    bool          =			field(
			    default=_A  ,							metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }  ,							)
			__UpperCamelCase   :    float          =			field(
			    default=0.75  ,							metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}	)
			__UpperCamelCase   :    bool          =			field(
			    default=_A  ,							metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}	)




@dataclass
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    float          =			field(
			    default=1E-3  ,							metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}	)







def 							A__       (    lowerCamelCase    )	->						Any:
				UpperCamelCase_:				Optional[Any]						=    torch.stack([example["""pixel_values"""] for example in examples]    )
				return {"pixel_values": pixel_values}







def 							A__       (    )	->						int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				UpperCamelCase_:				List[str]						=    HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)    )
				if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
				else:
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("""run_mae"""  ,     lowerCamelCase  ,     lowerCamelCase    )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,     datefmt="""%m/%d/%Y %H:%M:%S"""  ,     handlers=[logging.StreamHandler(sys.stdout    )]  ,     )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				UpperCamelCase_:				Optional[Any]						=    training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase    )
				transformers.utils.logging.set_verbosity(lowerCamelCase    )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}'''    )
				logger.info(F'''Training/evaluation parameters {training_args}'''    )

				# Detecting last checkpoint.
				UpperCamelCase_:				Dict						=    None
				if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
								UpperCamelCase_:				Dict						=    get_last_checkpoint(training_args.output_dir    )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    """Use --overwrite_output_dir to overcome."""    )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""    )

    # Initialize our dataset.
				UpperCamelCase_:				List[Any]						=    load_dataset(
				    data_args.dataset_name  ,     data_args.dataset_config_name  ,     data_files=data_args.data_files  ,     cache_dir=model_args.cache_dir  ,     use_auth_token=True if model_args.use_auth_token else None  ,     )

				# If we don't have a validation split, split off a percentage of train as validation.
				UpperCamelCase_:				Union[str, Any]						=    None if """validation""" in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split  ,     lowerCamelCase    ) and data_args.train_val_split > 0.0:
								UpperCamelCase_:				Optional[Any]						=    ds["""train"""].train_test_split(data_args.train_val_split    )
								UpperCamelCase_:				int						=    split["""train"""]
								UpperCamelCase_:				List[Any]						=    split["""test"""]

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				UpperCamelCase_:				Optional[int]						=    {
				    """cache_dir""": model_args.cache_dir,
				    """revision""": model_args.model_revision,
				    """use_auth_token""": True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								UpperCamelCase_:				int						=    ViTMAEConfig.from_pretrained(model_args.config_name  ,     **lowerCamelCase    )
				elif model_args.model_name_or_path:
								UpperCamelCase_:				List[str]						=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,     **lowerCamelCase    )
				else:
								UpperCamelCase_:				List[Any]						=    ViTMAEConfig()
								logger.warning("""You are instantiating a new config instance from scratch."""    )
								if model_args.config_overrides is not None:
												logger.info(F'''Overriding config: {model_args.config_overrides}'''    )
												config.update_from_string(model_args.config_overrides    )
												logger.info(F'''New config: {config}'''    )

    # adapt config
				config.update(
				    {
				        """mask_ratio""": model_args.mask_ratio,
				        """norm_pix_loss""": model_args.norm_pix_loss,
				    }    )

				# create image processor
				if model_args.image_processor_name:
								UpperCamelCase_:				Tuple						=    ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,     **lowerCamelCase    )
				elif model_args.model_name_or_path:
								UpperCamelCase_:				int						=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,     **lowerCamelCase    )
				else:
								UpperCamelCase_:				Dict						=    ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								UpperCamelCase_:				Optional[Any]						=    ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path  ,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )  ,     config=lowerCamelCase  ,     cache_dir=model_args.cache_dir  ,     revision=model_args.model_revision  ,     use_auth_token=True if model_args.use_auth_token else None  ,     )
				else:
								logger.info("""Training new model from scratch"""    )
								UpperCamelCase_:				List[str]						=    ViTMAEForPreTraining(lowerCamelCase    )

				if training_args.do_train:
								UpperCamelCase_:				int						=    ds["""train"""].column_names
				else:
								UpperCamelCase_:				Dict						=    ds["""validation"""].column_names

				if data_args.image_column_name is not None:
								UpperCamelCase_:				Union[str, Any]						=    data_args.image_column_name
				elif "image" in column_names:
								UpperCamelCase_:				Tuple						=    """image"""
				elif "img" in column_names:
								UpperCamelCase_:				List[str]						=    """img"""
				else:
								UpperCamelCase_:				List[Any]						=    column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								UpperCamelCase_:				Optional[int]						=    image_processor.size["""shortest_edge"""]
				else:
								UpperCamelCase_:				str						=    (image_processor.size["""height"""], image_processor.size["""width"""])
				UpperCamelCase_:				Tuple						=    Compose(
				    [
				        Lambda(lambda lowerCamelCase    : img.convert("""RGB"""    ) if img.mode != "RGB" else img    ),
				        RandomResizedCrop(lowerCamelCase  ,     scale=(0.2, 1.0)  ,     interpolation=InterpolationMode.BICUBIC    ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean  ,     std=image_processor.image_std    ),
				    ]    )

				def preprocess_images(lowerCamelCase    ):
								UpperCamelCase_:				List[str]						=    [transforms(lowerCamelCase    ) for image in examples[image_column_name]]
								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError("""--do_train requires a train dataset"""    )
								if data_args.max_train_samples is not None:
												UpperCamelCase_:				str						=    ds["""train"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
								# Set the training transforms
								ds["train"].set_transform(lowerCamelCase    )

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError("""--do_eval requires a validation dataset"""    )
								if data_args.max_eval_samples is not None:
												UpperCamelCase_:				Tuple						=    (
												    ds["""validation"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
												)
								# Set the validation transforms
								ds["validation"].set_transform(lowerCamelCase    )

				# Compute absolute learning rate
				UpperCamelCase_:				List[str]						=    (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								UpperCamelCase_:				str						=    training_args.base_learning_rate * total_train_batch_size / 2_56

				# Initialize our trainer
				UpperCamelCase_:				int						=    Trainer(
				    model=lowerCamelCase  ,     args=lowerCamelCase  ,     train_dataset=ds["""train"""] if training_args.do_train else None  ,     eval_dataset=ds["""validation"""] if training_args.do_eval else None  ,     tokenizer=lowerCamelCase  ,     data_collator=lowerCamelCase  ,     )

				# Training
				if training_args.do_train:
								UpperCamelCase_:				Dict						=    None
								if training_args.resume_from_checkpoint is not None:
												UpperCamelCase_:				str						=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												UpperCamelCase_:				Any						=    last_checkpoint
								UpperCamelCase_:				int						=    trainer.train(resume_from_checkpoint=lowerCamelCase    )
								trainer.save_model()
								trainer.log_metrics("""train"""  ,     train_result.metrics    )
								trainer.save_metrics("""train"""  ,     train_result.metrics    )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								UpperCamelCase_:				Any						=    trainer.evaluate()
								trainer.log_metrics("""eval"""  ,     lowerCamelCase    )
								trainer.save_metrics("""eval"""  ,     lowerCamelCase    )

				# Write model card and (optionally) push to hub
				UpperCamelCase_:				Tuple						=    {
				    """tasks""": """masked-auto-encoding""",
				    """dataset""": data_args.dataset_name,
				    """tags""": ["""masked-auto-encoding"""],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCamelCase    )
				else:
								trainer.create_model_card(**lowerCamelCase    )







def 							A__       (    lowerCamelCase    )	->						Any:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Dict    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""dpr"""

			def __init__(							self			:							str    ,						snake_case_			:							Any=3_0522    ,						snake_case_			:							List[str]=768    ,						snake_case_			:							List[str]=12    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Tuple=3072    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[Any]=0.1    ,						snake_case_			:							str=0.1    ,						snake_case_			:							Dict=512    ,						snake_case_			:							int=2    ,						snake_case_			:							Union[str, Any]=0.02    ,						snake_case_			:							str=1e-12    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Any="absolute"    ,						snake_case_			:							int = 0    ,						**snake_case_			:							int    ,						):
							super().__init__(pad_token_id=snake_case_    ,						**snake_case_   )

							UpperCamelCase_:				str						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Any						=    num_attention_heads
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Optional[Any]						=    intermediate_size
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Tuple						=    attention_probs_dropout_prob
							UpperCamelCase_:				str						=    max_position_embeddings
							UpperCamelCase_:				Any						=    type_vocab_size
							UpperCamelCase_:				Tuple						=    initializer_range
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Tuple						=    projection_dim
							UpperCamelCase_:				Dict						=    position_embedding_type






